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Poster

Metrizing Weak Convergence with Maximum Mean Discrepancies

Carl-Johann Simon-Gabriel · Alessandro Barp · Bernhard Schölkopf · Lester Mackey

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Thu 12 Dec 11 a.m. PST — 2 p.m. PST

Abstract: This paper characterizes the maximum mean discrepancies (MMD) that metrize the weak convergence of probability measures for a wide class of kernels. More precisely, we prove that, on a locally compact, non-compact, Hausdorff space, the MMD of a bounded continuous Borel measurable kernel $k$, whose RKHS-functions vanish at infinity (i.e., $H_k \subset C_0$), metrizes the weak convergence of probability measures if and only if $k$ is continuous and integrally strictly positive definite ($\int$s.p.d.) over all signed, finite, regular Borel measures. We also correct a prior result of Simon-Gabriel and Schölkopf (JMLR 2018, Thm. 12) by showing that there exist both bounded continuous $\int$s.p.d. kernels that do not metrize weak convergence and bounded continuous non-$\int$s.p.d. kernels that do metrize it.

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